To induce dataset imbalance, we half the number of samples per class in Mini-ImageNet. In table REF  (left), we show the accuracy after training via standard episodic meta-training [1]} with (balanced) 5-shot 5-way tasks. In this particular scenario, we use significantly higher imbalance levels (\(\rho =19\) ) compared to those in the previous section; despite this, we observe small, insignificant performance differences between balanced and imbalanced conditions.
In additional experiments, we further reduced the dataset size to contain a total of 4800 images and 32 randomly selected classes. In Figure REF  (Appendix REF ), we observe a more significant performance drop as we increase the number of minority classes. Meta-evaluating on CUB showed a similar trend, with an average drop of \(-1.6\%\)  on the most extreme imbalance setting: 30-510 step with 24 minority classes and \(\rho =17.0\)  (Appendix REF ). When we breakdown the results by model in Appendix REF , we observe that optimization-based approaches and fine-tune baselines have a slight advantage over the metric-based, most likely due to the ability to adapt during inference. Interestingly, in this setting RelationNet performs the worse with a drop of \(-4.3\%\)  w. r. t. the balanced task in the most extreme setting (24 minority, Mini-ImageNet).
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